Technology Trend Analysis and prediction for word Data From 2011-2018

library("plyr")
library("dplyr")


library("tidyr")
#library("plyr")

Data<-read.csv("/Users/ranjitarajeevashetty/Documents/Summer_2018/RTBD_Project/Visualization/data_n.csv", header = TRUE,stringsAsFactors = FALSE)


#renameing


tech_node<- Data$Tech

tech_node<-as.data.frame(tech_node)

related_node<-Data$Related

related_node<- as.data.frame(related_node)

names(tech_node) <- c("id")

names(related_node)<- c("id")

#rbind

#total <- rbind(data frameA, data frameB) 


total_nodes <- rbind(tech_node, related_node)

#deduped.data <- unique( yourdata[ , 1:3 ] )
### assuming df holds your data
#df.unique <- df[!duplicated(df$playerid), ]

total_nodes_deduped<-total_nodes[!duplicated(total_nodes), ]

total_nodes_deduped<-as.data.frame(total_nodes_deduped)

names(total_nodes_deduped)<- c("id")

#this is my node


names(total_nodes_deduped)[0]<-"group"

#total_nodes_deduped[-(1:2),][0]<-"group"

total_nodes_deduped$group<- "Data"


main_linkk<-Data%>%group_by(Tech,Related)%>% tally()%>%arrange(desc(n))

library(igraph)

data_ver<-read.csv("/Users/ranjitarajeevashetty/Documents/Summer_2018/RTBD_Project/Visualization/vertices_n.csv", header = TRUE,stringsAsFactors = FALSE)

main_linkk<-merge(x = main_linkk, y = data_ver, by.x= "Tech" , by.y ="Tech", all.x = TRUE )

#data_vertex<-data_vertex$Tech

data_net<- graph_from_data_frame(d=main_linkk, vertices=total_nodes_deduped, directed=T) 


#V(data_net)$color[which(V(data_net)$Tech == "Data-2013")] <- 'blue'


library(visNetwork)


#library(dplyr)
library(igraph)

library(visNetwork)

visIgraph(data_net, layout ="layout_nicely")%>%visGroups(groupname = "Data", color = "deepskyblue", shape = "circle")%>%visOptions(highlightNearest = TRUE) %>%visLegend()%>%
  
  visPhysics(stabilization = TRUE, solver = "barnesHut", barnesHut = list(gravitationalConstant = -5000, avoidOverlap=1))

Language Trend Analysis From 2011-2018

library(ggplot2)
library(plotly)

LanguageTrend<- read.csv("/Users/ranjitarajeevashetty/Documents/Summer_2018/RTBD_Project/Visualization/LanguageTrend.csv", header = TRUE,stringsAsFactors = FALSE)

#ime vs Occupancy
plot_ly(LanguageTrend, x = ~Time_Period , y = ~word_count, name='',type = 'scatter', mode='lines',
              color = ~Prog_lang)%>%layout(title=" Language Trend",xaxis=list(title="",tickangle=45 ),yaxis=list(title="Language"))

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.